Diffusion-weighted imaging (DWI) has been known for its ability to provide a unique tissue contrast that can be used for early detection of ischemic stroke. Diffusion Tensor Imaging (DTI) is an extension of DWI whereby multiple diffusion-weighted measurements are applied to obtain a diffusion tensor for each pixel. Because of prolonged scan times and motion sensitivity of diffusion weighting gradients, countermeasures to prevent motion-induced artifacts in DTI are important for the success of a study. It is helpful to briefly review some aspects of DWI and DTI. In DWI, a pulsed magnetic field gradient is applied in a specified direction, and then a gradient pulse in the same direction but with opposite magnitude is applied some time later. An alternative and more frequently used MR pulse sequence for DWI is to use two gradient pulses of identical polarity straddling a 180 degree refocusing RF pulse. The resulting image depends on the diffusion constant of the object being imaged in the specified direction. This process can be referred to as “diffusion encoding”. For example, the specified direction could be the z-axis, and the resulting image would show contrast depending on how well (or poorly) protons diffuse in the z-direction. Any other direction could also be chosen to provide a DWI image. In diffusion tensor imaging, enough DWI images are obtained to enable a determination of a diffusion tensor for each voxel of the object being imaged. The number of DWI images required depends on the diffusion tensor model employed. For example, if a symmetric second order diffusion tensor is assumed, then at least 6 DWI images with diffusion-encoding along non collinear directions are required for DTI.
Phase contrast MRI is similar to DWI, in the sense that directional qualities of the object affect image contrast. However, in phase contrast MRI, the underlying physical principle is different than in DWI. Phase contrast MRI is based on the property that motion of tissue in a magnetic field gradient produces a change in the net MR signal phase. As in the DWI case described above, the specified direction of the magnetic field gradient sets up a “velocity encoding”. For example, the specified direction could be the z-axis, and the resulting image would show contrast depending on tissue motion in the z-direction. Any other gradient direction could also be chosen to provide a phase contrast MRI image with velocity-sensitivity along this direction. For example, the three components of tissue velocity can be obtained from 3 phase contrast MRI images having orthogonal field gradient directions (e.g., x, y, and z gradients+one reference measurement). Alternative strategies, such as Hadamard gradient encoding, can be also used.
Both DWI/DTI and phase contrast MRI are adversely affected by object/patient motion during data acquisition. One way to eliminate the effects of motion (especially for DWI/DTI due to its profound susceptibility to even smallest amount of bulk motion) is to “freeze” the anatomy being imaged by using snapshot imaging techniques—the most popular of which is single shot EPI (sshEPI). However, susceptibility artifacts due to the very low bandwidth per pixel in the phase-encode direction and T2*-induced blurring caused by the long EPI readout substantially limit the applicability of this method. To address the limitations of single-shot EPI, the use of parallel imaging has been suggested. Nevertheless, the maximum speedup in the phase encode direction that can be achieved by parallel imaging is limited and, thus, the degree of distortion reduction. Another method employed frequently is to use interleaved k-space acquisitions to traverse faster through k-space and, in this way, to reduce the aforementioned distortions. However, diffusion-weighted multi-shot sequences are very sensitive to motion. Each interleaf accrues a different (typically non-linear) phase in the image domain. Navigator based methods that utilize 1D navigators, 2D navigators or self-navigating trajectories have demonstrated variable efficacy to eliminate these motion artifacts.
While the phase-navigation used in DTI is usually focused on minuscule motion (i.e. motion in a range that affects image phase but not pixel position), gross patient motion becomes a concern—especially in the case of uncooperative patients such as children and patients that suffer from a specific medical condition (e.g. stroke) that keeps them from staying stationary. Mere misregistration can be corrected for by advanced registration methods. However, motion exposes the object to a different diffusion encoding gradient than the desired one. Neglecting the change in the diffusion encoding caused by motion can cause erroneous estimations of the diffusion tensor orientation and anisotropy.
FIGS. 1a-b illustrate this problem. Let bi denote the applied diffusion encoding and let pi represent the tensor orientation, which depends on subject motion. In case of no motion (FIG. 1a), the estimated diffusion tensor (dotted line) is the same as the actual tensor 102 (solid line). If there is rotational motion (FIG. 1b), the estimated tensor orientation 104 can be different from the actual tensor orientations p1, p2, p3, and the anisotropy will be lower than the actual values. The error in Fractional Anisotropy (FA) and orientation will depend on the severity of the motion and the pattern of orientation changes.
The studies on the consequences of gross motion during diffusion encoding are limited; mostly because distortions on a much smaller scale (e.g. brain pulsation and miniscule bulk motion) have often been more of a concern. Recently, an approach to correct for these deviations from the desired encoding has been suggested for sshEPI by Rohde et al., in “Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI”, Magnetic Resonance in Medicine, 2004; 51:103-114. In this work, the b-matrix for a single-shot data set is computed, and then rotated to the target coordinates. In this manner, several single-shot data sets, each possibly having different rotations, can be made consistent.
In considering the effect of gross patient motion for DTI, it is helpful to consider two cases. In case A, patient motion during each of the DWI scans is negligible, however, the DWI scans do not necessarily all have the same patient orientation. In case B, patient motion (especially rotation) is not negligible during the DWI scans. The approach of Rhode et al. cited above would be applicable to case A, because each DWI scan is single-shot data. The several DWI images can be made consistent with each other by performing b-matrix rotations as needed so that all DWI images are referred to a common coordinate system. However, the approach of Rhode et al. is not applicable in case B. If patient motion is not negligible during a DWI scan, then the accumulated data for a DWI scan cannot meaningfully be regarded as a DWI image having a specified direction, because this “specified direction” is not constant for the DWI data. Therefore, the conventional 2-step method for tensor estimation—that is 1) the reconstruction of individual diffusion weighted images followed by 2) multivariate regression—cannot be used for multi-shot acquisition of the DWI images.
Accordingly, it would be an advance in the art to provide methods for DWI, DTI, and related methods that are applicable in the case of non-negligible patient motion during data acquisition.